>
> Arthur wrote:-
>
> <snip>
> >Lets start with
> >
> > intake1 intake2 intake3 other ~ Trait Tr.line Tr.feedflock Tr.damage,
> > Tr.brtype Tr.sex !r Tr.sire
> >
> > 1 2 0
> > 1728 #no. of animals in the data
> > Tr 0 US E11 E12 E22 E31 E32 E33 E41 E42 E43 E44 !=1212213330
> >
> > Tr.sire 2
> > Tr 0 US G11 G12 G22 G31 G32 G33 G41 G42 G43 G44 !=4545546660
> > sire
> >
> >SO what we have here is setting the model up as standard multivariate
> >and then constraining the intake variances and covariances.
> >
> >You could of course fit it with unconstrained variances.
> >
> >The way the first model was fitted, the 'units' term gave the E12 E32 R32
> >covariances and the 'units + residual' gave the E11, E22 and E33 terms.
> >
> >The sire term gave G11=G21=G22=G31=G32=G33
> >
> >When you get this running, you may see how to extend to more complex models.
>
Ron wrote
> Okay, I can see how that might work.
>
> But for a repeatability model, you need to have the
> genetic correlation equal to one among the 3 intake
> traits. I don't see how you do this, I know it
> uses the and() function in some way, but I don't understand it.
>
> Ron.
Arthur replies:
I'm not convinced you need to have a genetic correlation of 1 but
if you do, and want to fit the model, the way would be to fit
an equivalent model:
... !r sire Tr|4/sire ...
sire 2
2 0 US S11 S21 S22
sire
Under this model, the genetic variance for intake is S11
for other is S11 + S22
covariance is S11+S21 \
For computational ease, you might like to scale the unrepeated trait
so that its sire variance is greater than the sire variance for intake
[so that S22 has a positive value]
Arthur
>
<><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><>
Arthur Gilmour PhD email: Arthur.Gilmour@agric.nsw.gov.au
Senior Research Scientist (Biometrics) fax: <61> 2 6391 3899
NSW Agriculture telephone work: <61> 2 6391 3815
Orange Agricultural Institute home: <61> 2 6362 0046
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